Update README.md

README.md

@@ -1,22 +1,21 @@
 ---
-base_model: mistralai/Mistral-7B-v0.3
 extra_gated_description: >-
   If you want to learn more about how we process your personal data, please read
   our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
 ---
 
-# Model Card for Mistral-7B-Instruct-v0.3
+# Model Card for Mistral-Small-Instruct-2409
 
-The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
+Mistral-Small-Instruct-2409 is an instruct fine-tuned version with the following key characteristics:
 
-Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
-- Extended vocabulary to 32768
-- Supports v3 Tokenizer
+- 22B parameters
+- Vocabulary to 32768
 - Supports function calling
+- 128k sequence length
 
 ## Installation
 
-It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
+It is recommended to use `mistralai/Mistral-Small-Instruct-2409` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
 
 ```
 pip install mistral_inference
@@ -28,10 +27,10 @@ pip install mistral_inference
 from huggingface_hub import snapshot_download
 from pathlib import Path
 
-mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3')
+mistral_models_path = Path.home().joinpath('mistral_models', '22B-Instruct-Small')
 mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+snapshot_download(repo_id="mistralai/Mistral-Small-Instruct-2409", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
 ```
 
 ### Chat
@@ -39,7 +38,7 @@ snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=[
 After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
 
 ```
-mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
+mistral-chat $HOME/mistral_models/22B-Instruct-Small --instruct --max_tokens 256
 ```
 
 ### Instruct following
@@ -118,77 +117,6 @@ result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
 print(result)
 ```
 
-## Generate with `transformers`
-
-If you want to use Hugging Face `transformers` to generate text, you can do something like this.
-
-```py
-from transformers import pipeline
-
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"},
-]
-chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
-chatbot(messages)
-```
-
-
-## Function calling with `transformers`
-
-To use this example, you'll need `transformers` version 4.42.0 or higher. Please see the 
-[function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling)
-in the `transformers` docs for more information.
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-model_id = "mistralai/Mistral-7B-Instruct-v0.3"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-def get_current_weather(location: str, format: str):
-    """
-    Get the current weather
-
-    Args:
-        location: The city and state, e.g. San Francisco, CA
-        format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
-    """
-    pass
-
-conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
-tools = [get_current_weather]
-
-
-# format and tokenize the tool use prompt 
-inputs = tokenizer.apply_chat_template(
-            conversation,
-            tools=tools,
-            add_generation_prompt=True,
-            return_dict=True,
-            return_tensors="pt",
-)
-
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
-
-inputs.to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=1000)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool
-results to the chat history so that the model can use them in its next generation. For a full tool calling example, please
-see the [function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling), 
-and note that Mistral **does** use tool call IDs, so these must be included in your tool calls and tool results. They should be
-exactly 9 alphanumeric characters.
-
-
-## Limitations
-
-The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. 
-It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
-make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
 
 ## The Mistral AI Team